COVID-19 INDONESIA UPDATE

---
title: "COVID-19 INDONESIA UPDATE"
output: 
  flexdashboard::flex_dashboard:
    vertical_layout: scroll
    social: menu
    source_code: embed
---

Visualisasi perkembangan kasus Covid-19 di Indonesia. Data harian bersumber dari [covid19.go.id](https://data.covid19.go.id/) dan data perprovinsi dari [BNPB](https://bnpb-inacovid19.hub.arcgis.com/).
Last update : `r format(Sys.Date(), "%Y-%m-%d")`



```{r global, include=FALSE}
library(flexdashboard)

source("R/function.R")
source("R/variables.R")

ina_daily <- getData(INA_DAILY) %>% cleanData() %>% mutate(Tanggal = as.Date(harian.key_as_string))

ina_province <- getData(INA_PROVINCE) %>% cleanData()

update_date <- UPDATE_DATE
```








### TOTAL TERKONFIRMASI

```{r confirmed valuebox}
confirmed <- ina_daily %>% subset(Tanggal == update_date, select = total.jumlah_positif) %>%
    format(round(0), big.mark = ",")

confirmed_new <- ina_daily %>% subset(Tanggal==update_date, select = penambahan.jumlah_positif) %>%
    format(round(0), big.mark = ",")

valueBox(confirmed, caption = sprintf("TERKONFIRMASI  (%s)", if(confirmed_new>0) paste0("+",confirmed_new) else confirmed_new), icon = "fa fa-thermometer-full")
```

### TOTAL DIRAWAT

```{r hospitilized valuebox}
hospitilized <- ina_daily %>% subset(Tanggal==update_date, select = total.jumlah_dirawat) %>%
    format(round(0), big.mark = ",")

hospitilized_new <- ina_daily %>% subset(Tanggal==update_date, select = penambahan.jumlah_dirawat) %>%
    format(round(0), big.mark = ",")

valueBox(hospitilized, caption = sprintf("DALAM PERAWATAN  (%s)", if(hospitilized_new>0) paste0("+",hospitilized_new) else hospitilized_new), icon = "fa fa-hospital-o", color = "orange")
```


### TOTAL SEMBUH

```{r recover valuebox}
recovered <- ina_daily %>% subset(Tanggal==update_date, select = total.jumlah_sembuh) %>%
    format(round(0), big.mark = ",")

recovered_new <- ina_daily %>% subset(Tanggal==update_date, select = penambahan.jumlah_sembuh) %>%
    format(round(0), big.mark = ",")

valueBox(recovered, caption = sprintf("SEMBUH  (%s)", if(recovered_new>0) paste0("+",recovered_new) else recovered_new), icon = "fa fa-heart", color = "green")
```

### TOTAL MENINGGAL

```{r death valuebox}
death <- ina_daily %>% subset(Tanggal==update_date, select = total.jumlah_meninggal) %>%
    format(round(0), big.mark = ",")

death_new <- ina_daily %>% subset(Tanggal==update_date, select = penambahan.jumlah_meninggal) %>%
    format(round(0), big.mark = ",")

valueBox(death, caption = sprintf("MENINGGAL  (%s)", if(death_new>0) paste0("+",death_new) else death_new), icon = "fa fa-plus-square", color = "red")

```





### TREND KASUS KUMULATIF {data-padding=10}

```{r cummulative trend}
library(dplyr)
library(dygraphs)
library(xts)

dygraph_df <- ina_daily %>%
  select(Tanggal, harian.jumlah_positif_kum.value, harian.jumlah_sembuh_kum.value, harian.jumlah_meninggal_kum.value)%>%
  filter(Tanggal < update_date + 1) %>%
  rename(TERKONFIRMASI = harian.jumlah_positif_kum.value, SEMBUH = harian.jumlah_sembuh_kum.value, MENINGGAL = harian.jumlah_meninggal_kum.value)

xts_df <- xts(dygraph_df[,-1], dygraph_df[,1])
dygraph_plot_cum <- xts_df %>%  
  dygraph(main = "TREND KASUS KUMULATIF", ylab = "Total Kasus") %>% 
  dyRangeSelector(height = 20) %>%
  dyGroup(c("TERKONFIRMASI", "SEMBUH", "MENINGGAL"), drawPoints = TRUE, color = c("blue", "green", "red")) %>%
  dyLegend(width = 300) %>% 
  dyShading(from = "2020/04/10", to = "2020-06-01", color = "#FFE6E6") %>%
  dyShading(from = "2020/06/01", to = max(ina_daily$Tanggal), color = "#CCEBD6") %>% 
  dyEvent("2020/04/10", "Pemberlakuan PSBB", labelLoc = "bottom") %>%
  dyEvent("2020/05/23", "Lebaran", labelLoc = "bottom") %>%
  dyEvent("2020/06/01", "New Normal / Transisi / AKB", labelLoc = "bottom") 
  
dygraph_plot_cum

```

### TREND KASUS BARU PERHARI

```{r daily trend}
library(dplyr)
library(dygraphs)
library(xts)

dygraph_df <- ina_daily %>% 
  filter(Tanggal < update_date + 1) %>%
  select(Tanggal, harian.jumlah_positif.value, harian.jumlah_sembuh.value, harian.jumlah_meninggal.value)%>%
  mutate(Tanggal=as.Date(Tanggal)) %>%
  rename(TERKONFIRMASI = harian.jumlah_positif.value, SEMBUH = harian.jumlah_sembuh.value, MENINGGAL = harian.jumlah_meninggal.value)

xts_df <- xts(dygraph_df[,-1], dygraph_df[,1])
dygraph_plot_new_case <- xts_df %>%  
  dygraph(main = "TREND KASUS BARU PERHARI", ylab = "Jumlah Kasus Baru") %>%
  dyGroup(c("TERKONFIRMASI", "SEMBUH", "MENINGGAL"), drawPoints = TRUE, color = c("blue", "green", "red")) %>%
  dyRangeSelector(height = 20) %>%
  dyLegend(width = 300) %>% 
  dyShading(from = "2020/04/10", to = "2020/06/01", color = "#FFE6E6") %>%
  dyEvent("2020/05/23", "Lebaran", labelLoc = "bottom") %>%
  dyShading(from = "2020/06/01", to = max(ina_daily$Tanggal), color = "#CCEBD6") %>% 
  dyEvent("2020/04/10", "Pemberlakuan PSBB", labelLoc = "bottom") %>%
  dyEvent("2020/05/23", "Lebaran", labelLoc = "bottom") %>%
  dyEvent("2020/06/01", "New Normal / Transisi / AKB", labelLoc = "bottom")

dygraph_plot_new_case

```









### TREND PERPROVINSI

```{r daily trend by province}
library(RcppRoll)

# code refference https://github.com/dioariadi/covid-19/blob/master/covid-19-tracker-indonesia.Rmd

case_by_prov <- ina_province %>%
  select(Provinsi, Tanggal, Penambahan_Harian_Kasus_Terkonf) %>%
  filter(Provinsi != "Indonesia") %>%
  mutate(Tanggal = as.Date(Tanggal)) %>%
  filter(Tanggal != "2020-08-03" & Tanggal < "2020-08-10") %>%
  arrange(Tanggal) %>% 
  group_by(Provinsi) %>%
    mutate(casesroll_avg=round(roll_mean(Penambahan_Harian_Kasus_Terkonf, 7, align="right", fill=0),0)) %>%
  ungroup()

filter_daily <- case_by_prov %>%
  group_by(Provinsi) %>%
  filter(casesroll_avg >= 5) %>%
  slice(min(which(casesroll_avg >= 5))) %>% 
  ungroup() %>% 
  #mutate(Tanggal = as.Date(Tanggal)) %>%
  rename(filter_week = Tanggal)

case_by_prov_daily <- case_by_prov %>% ungroup() %>% 
   left_join(filter_daily[,c(1,2)], by = c("Provinsi")) %>% 
   filter(Tanggal >= filter_week) %>% 
   arrange(Tanggal) %>% 
   group_by(Provinsi) %>% 
   mutate(day_since_n_case = row_number()) %>% 
   ungroup()
```


```{r daily trend by province plot}
library(highcharter)

case_prov_daily_plot <- hchart(case_by_prov_daily, "line", 
                         hcaes(x = day_since_n_case, y = casesroll_avg, group = Provinsi),
                         showInLegend = FALSE) %>%
  hc_xAxis(
    title = list(text = "Jumlah Hari Sejak Rata-rata Kasus Perhari diatas 4"),
    gridLineWidth = 0
    ) %>%
  hc_yAxis(
    title = list(text = "Penambahan Kasus Perhari"),
    type = "linear",
    min = 0,
    accessibility = list(
            rangeDescription = 'Range: 1 to 1000'),
    gridLineWidth = 0
    ) %>%
  hc_title(
    style = list(color = hex_to_rgba("black", 0.7)),
    text = "Penambahan Kasus Perprovinsi"
    ) %>%
  hc_subtitle(
    style = list(color = hex_to_rgba("black", 0.5)),
    text = "Rolling Average 7 hari Penambahan Kasus Terkonfirmasi Perhari"
    ) %>%
  hc_caption(
    text = sprintf("Data as of : %s", max(case_by_prov$Tanggal))
    )

case_prov_daily_plot
```

> NOTE : Sejak tanggal 10 Agustus data dari BNPB tidak sesuai dengan data resmi gugus tugas sehingga kurvanya menjadi anomali dan tidak valid.


### PROVINSI STATUS

```{r province status}
prov_status <- ina_province %>% 
  select(Provinsi, Tanggal, Kasus_Terkonfirmasi_Akumulatif, Kasus_Sembuh_Akumulatif, Kasus_Meninggal_Akumulatif, Kasus_Aktif_Akumulatif)  %>% 
    filter(Provinsi != "Indonesia") %>%
    filter(as.Date(Tanggal) == max(as.Date(Tanggal))) %>%
    mutate(death_rate = round(Kasus_Meninggal_Akumulatif / Kasus_Terkonfirmasi_Akumulatif, 4) * 100) %>%
    mutate(recovery_rate = round(Kasus_Sembuh_Akumulatif / Kasus_Terkonfirmasi_Akumulatif, 4) * 100)

```


```{r province status plot}
library(highcharter)
#colorised status based on death_rate dan recovery_rate
prov_status$Status <- with(prov_status, ifelse(
  death_rate < mean(prov_status$death_rate) & recovery_rate < mean(prov_status$recovery_rate), 'LowDeathLowRecover', ifelse(
  death_rate > mean(prov_status$death_rate) & recovery_rate < mean(prov_status$recovery_rate), 'HighDeathLowRecover', ifelse(
 death_rate > mean(prov_status$death_rate) & recovery_rate > mean(prov_status$recovery_rate), 'HighDeathHighRecover', ifelse(
  death_rate < mean(prov_status$death_rate) & recovery_rate > mean(prov_status$recovery_rate), 'LowDeathHighRevover', 'Others')))))
colors <- c('#FFA500', '#FF4500', '#7FFF00', '#FFD700', '#FFC100')
x <- c("Provinsi", "Recovery Rate", "Death Rate", "Kasus Aktif")
#y <- sprintf("{point.%s:.2f}", c("Provinsi", "recovery_rate", "death_rate", "Kasus_Aktif_Akumulatif"))
y <- c("{point.Provinsi}", "{point.recovery_rate}%", "{point.death_rate}%", "{point.Kasus_Aktif_Akumulatif}")
tooltip_tb <- tooltip_table(x, y)
hchart(
  prov_status,
  "scatter",
  hcaes(
    as.numeric(recovery_rate),
    as.numeric(death_rate), 
    size = Kasus_Terkonfirmasi_Akumulatif,
    group = Status
    ),
  minSize = 15,
  maxSize = 55
  ) %>%
  hc_chart(
     backgroundColor = "white",
    backgroundColor = hex_to_rgba("white", 0.5)
    ) %>%
  hc_colors(colors = colors) %>%
  hc_xAxis(
    title = list(text = "Recovery Rate"),
    labels = list(format = '{value}%'),
    plotLines = list(list(
      value = mean(prov_status$recovery_rate),
      label = list(
        text = sprintf("Rata-rata Nasional (%s%%)", round(mean(prov_status$recovery_rate),2)),
        rotation = 0,
        style = list(fontStyle = 'italic')),
      color = 'black',
      width = 2,
      dashStyle = 'dot'
    )),
    gridLineWidth = 0,
    reversed = FALSE
    ) %>%
  hc_yAxis(
    title = list(text = "Death Rate"),
    labels = list(format = '{value}%'), 
    min = 0,
    max = max(prov_status$death_rate) + 1,
    plotLines = list(list(
      value = mean(prov_status$death_rate),
      label = list(
        text = sprintf("Rata-rata Nasional (%s%%)", round(mean(prov_status$death_rate),2)),
        rotation = 0,
        style = list(fontStyle = 'italic')),
      color = 'black',
      width = 2,
      dashStyle = 'dot'
    )), 
    gridLineWidth = 0
    ) %>%
  hc_title(
    style = list(color = hex_to_rgba("black", 0.7)),
    text = "Status Perprovinsi"
    ) %>%
  hc_subtitle(
    style = list(color = hex_to_rgba("black", 0.5)),
    text = "Berdasarkan Fatality Rate"
    ) %>% 
  hc_caption(
    text = sprintf("Data as of : %s", max(case_by_prov$Tanggal))
    ) %>%
  #
  # hc_plotOptions(
  #   series = list(
  #           dataLabels = list(
  #               enabled = TRUE,
  #               format = '{point.Provinsi}',
  #               allowOverlap = TRUE,
  #               style = list(
  #                 color = "black",
  #                 fontSize = "9px",
  #                 fontWeight = "bold",
  #                 textOutline = "0.5px contrast"
  #                 )
  #           )
  #   )
  #) %>%
  hc_tooltip(
    useHTML = TRUE,
    headerFormat = "",
    pointFormat = tooltip_tb
    ) %>%
  hc_plotOptions(
        series = list(
            maxSize = 70)
  )

```

> NOTE : Sejak tanggal 10 Agustus data dari BNPB tidak sesuai dengan data resmi gugus tugas sehingga kurvanya menjadi anomali dan tidak valid.